This is an idea proposed in 2024 as a good starter project, and is currently being worked on by Radhika Iyer. It is supervised by Sadiq Jaffer and Anil Madhavapeddy as part of my Conservation Evidence Copilots project.
At the Conservation Evidence Copilots project, we are interested in finding and synthesising evidence for conservation interventions. Once we have this evidence, it needs to be synthesised into a form that can be used to inform policy and practice via natural language query interfaces. One way to do this is to use a RAG (Retrieval Augmented Generation) pipeline, which can automatically retrieve relevant information from a large corpus of documents, analyse it to extract key information relevant to CE, and generate a summary of the key findings.
This project will involve evaluating the performance of RAG pipelines for conservation evidence, comparing different models, configurations and benchmark sets, and identifying areas for improvement. The goal is to develop a set of best practices for using RAG pipelines in the context of conservation evidence, and to make recommendations for future work in this area.